Comparison between the ages for the types (after controlling for gender)

Questions

  • What are the differences between the ages for the different types
  • Do we observe the same changes as globally?

Age effect - General Questions

  • What are the differences between the ages?
  • Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
  • Are they different for the two genders?
  • Are they different for the two types?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

  1. 14447
  2. 88
  1. 0.3609065973726
  2. 0
  1. 4.84577258072278
  2. 0.21654395842356

Differentially expressed genes

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$sign_fc_deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Stats

All DEG (Wald padj < 0.05)All over-expressed genes (Wald padj < 0.05 & FC > 0)All under-expressed genes (Wald padj < 0.05 & FC < 0)DEG (Wald padj < 0.05 & abs(FC) >= 1.5)Over-expressed genes (Wald padj < 0.05 & FC >= 1.5)Under-expressed genes (Wald padj < 0.05 & FC <= -1.5)
52w VS 8w (SPF)1205 718 487 574446 128
52w VS 8w (GF) 556 349 207 310233 77
104w VS 52w (SPF)1439 860 579 429272 157
104w VS 52w (GF)1276 646 630 573189 384
104w VS 8w (SPF)2938163613021261847 414
104w VS 8w (GF)2259119610631158550 608

All DEG (Wald padj < 0.05)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)

Log2FC

comptype
52w VS 8w (SPF)52w VS 8w SPF
52w VS 8w (GF)52w VS 8w GF
104w VS 52w (SPF)104w VS 52wSPF
104w VS 52w (GF)104w VS 52wGF
104w VS 8w (SPF)104w VS 8w SPF
104w VS 8w (GF)104w VS 8w GF

Z-score

Co-expression (WGCNA)

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated
52w VS 8w 104w VS 52w 104w VS 8w
SPF
GF

Z-score in modules

GO analysis

Biological process

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

52w VS 8w 104w VS 52w 104w VS 8w
SPF
GF

52w VS 8w (SPF)

GO Tree at "../results/dge/age-effect/age_type/go/52w_VS_8w_SPF.png"

52w VS 8w (GF)

GO Tree at "../results/dge/age-effect/age_type/go/52w_VS_8w_GF.png"

104w VS 52w (SPF)

GO Tree at "../results/dge/age-effect/age_type/go/104w_VS_52w_SPF.png"

104w VS 52w (GF)

GO Tree at "../results/dge/age-effect/age_type/go/104w_VS_52w_GF.png"

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 77 rows, data has 92
Traceback:

1. plot_kegg_pathways(age_type_deg$over_represented_KEGG[, "category"], 
 .     age_type_deg$fc_deg, "../results/dge/age-effect/age_type/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

SPF effect in aging

Question: Is there any genes that shows an increasing fold change in SPF with aging while not for GF?

52w vs 8w for SPF 52w vs 8w for GF 104w vs 52w for SPF 104w vs 52w for GF Gene number
Set 1 != != 244
Set 2 != == != == 92
Gene number
Set 1220
Set 2 91

Genes with differential expression with aging in SPF but not in GF (set2)

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0

[1] 28

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 21.858, df = 26, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9436093 0.9879760
sample estimates:
      cor 
0.9738524

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0

[1] 25

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -15.545, df = 23, p-value = 1.081e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9804861 -0.9003858
sample estimates:
       cor 
-0.9555577

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0

[1] 32

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -11.512, df = 30, p-value = 1.562e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9519558 -0.8090666
sample estimates:
       cor 
-0.9030072

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0

[1] 6

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 5.4385, df = 4, p-value = 0.005549
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5328604 0.9934268
sample estimates:
      cor 
0.9385478

Summary